How to set the encoder transfer function in autoencoder
6 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
I want to set the encoder transfer function by myself. But I do not how to do it. Can the encoder transfer function be changed by myself
0 Kommentare
Antworten (2)
SANA
am 16 Nov. 2018
First you make an autoencoder and generate its function, the code for generating the function of autoencoder is:
autoenc = trainAutoencoder(Data, 300,...
'MaxEpochs', 100,...
'L2WeightRegularization', 0.001,...
'SparsityRegularization', 4,...
'SparsityProportion', 0.05,...
'ScaleData', true);
generateFunction(autoenc)
The generated function open in MATLAB editor with the name of neural_function, I renamed it my_autoencoder and the transfer function is mentioned there, so you can edit it as you wish, code is below:
function [y1] = my_encoder(x1)
%NEURAL_FUNCTION neural network simulation function.
%
% Generated by Neural Network Toolbox function genFunction, 15-Nov-2018 15:50:26.
%
% [y1] = neural_function(x1) takes these arguments:
% x = 5088xQ matrix, input #1
% and returns:
% y = 5088xQ matrix, output #1
% where Q is the number of samples.
%#ok<*RPMT0>
% ===== NEURAL NETWORK CONSTANTS =====
% Input 1
x1_step1.xoffset = [-10;-11;-11;-12;-1]
x1_step1.gain = [0.090;0.9090;0.9090;0.90909;0.0769]
x1_step1.ymin = 0;
% Layer 1
b1 = [-0.115;0.768;0.7066;0.5009303396;0.1249019302]
IW1_1 = [-0.0947 0.7320 0.3146 0.494636173 -0.0951171]
b2 = [3.5409901027442902688;3.6635759144424437928]
LW2_1 = [-1.1707436273955371675 1.5786236406994880177 -1]
% Output 1
y1_step1.ymin = 0;
y1_step1.gain = [0.0909090909090909;0.07]
y1_step1.xoffset = [-10;-11;-11;-12;-12]
% ===== SIMULATION ========
% Dimensions
Q = size(x1,2); % samples
% Input 1
xp1 = mapminmax_apply(x1,x1_step1);
% Layer 1
a1 = logsig_apply(repmat(b1,1,Q) + IW1_1*xp1);
% Layer 2
a2 = logsig_apply(repmat(b2,1,Q) + LW2_1*a1);
% Output 1
y1 = mapminmax_reverse(a2,y1_step1);
end
% ===== MODULE FUNCTIONS ========
% Map Minimum and Maximum Input Processing Function
function y = mapminmax_apply(x,settings)
y = bsxfun(@minus,x,settings.xoffset);
y = bsxfun(@times,y,settings.gain);
y = bsxfun(@plus,y,settings.ymin);
end
% **********************************************
% ************ Enhance encoder here ************
% Sigmoid Positive Transfer Function
function a = logsig_apply(n,~)
a = 1 ./ (1 + exp(-n));
end
% ************ Enhance encoder here ************
% **********************************************
% Map Minimum and Maximum Output Reverse-Processing Function
function x = mapminmax_reverse(y,settings)
x = bsxfun(@minus,y,settings.ymin);
x = bsxfun(@rdivide,x,settings.gain);
x = bsxfun(@plus,x,settings.xoffset);
end
You can change decoder function as well. Enjoy !!!
Frédéric BERTHOMMIER
am 28 Sep. 2023
Bearbeitet: Frédéric BERTHOMMIER
am 28 Sep. 2023
I changed the decoder transfer function to recover a PCA equivalent which I checked::
autoenc = trainAutoencoder(Data,4,'MaxEpochs',5000,'DecoderTransferFunction','purelin');
This substitutes to the generation of a .m function as proposed before for obtaining a PCA equivalent. Note that the encoder transfer function cannot be modified.
0 Kommentare
Siehe auch
Kategorien
Mehr zu Pattern Recognition and Classification finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!